360D: A dataset and baseline for dense depth estimation from 360 images

Home360D: A dataset and baseline for dense depth estimation from 360 images

Abstract:

We present a baseline for 360o dense depth estimation from a single spherical panorama. We circumvent the unavailability of coupled 360o color and depth image datasets by rendering a high quality 360o dataset from existing 3D datasets. We then train a CNN designed speci_cally for 360o content in a supervised manner, in order to predict a 360o depth map from a single omnidirectional image in equirectangular format. Quantitative and qualitative results show the need for training directly in 360o instead of relying on traditional 2D CNNs.

The focus of the Visual Computing Laboratory is to develop new algorithms and architectures for applications in the areas of 3D processing, image/video processing, computer vision, pattern recognition, bioinformatics and medical imaging.